Train AI Models Across AWS Regions Without Moving Data

Amazon SageMaker HyperPod paired with Qumulo's Cloud Data Fabric lets teams train large AI models across AWS Regions without replicating datasets or modifying code. A spoke cluster in US West Oregon reads data stored in US East Ohio through VPC peering, with Qumulo's NeuralCache predicting and pre-caching data blocks to eliminate cross-Region latency. Validation tests running a 1.02 billion-parameter LLaMA v3 model showed the remote spoke cluster matched hub performance at 115-117 samples per second after a brief warmup of 100-150 batches. GPU utilization converges to 98-100 percent, with the initial warmup representing under 1 percent of total training time at scale.